91 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Beijing Academy of Artificial IntelligenceCN18 papers
- Peking UniversityCN11 papers
- Tsinghua UniversityCN5 papers
- University of California, Los AngelesUS5 papers
- Chinese Academy of SciencesCN2 papers
- Huazhong University of Science and TechnologyCN2 papers
- Queen Mary University of LondonGB2 papers
- Robotics Research (United States)US2 papers
- Beihang UniversityCN1 paper
- Beijing Jiaotong UniversityCN1 paper
- Chinese PLA General HospitalCN1 paper
- Chinese University of Hong Kong, ShenzhenCN1 paper
5 papers · 1 filter
Robust Transfer Learning with Pretrained Language Models through Adapters
Wenjuan Han, Bo Pang, Yingnian Wu
Transfer learning with large pretrained transformer-based language models like BERT has become a dominating approach for most NLP tasks. Simply fine-tuning those large language mod…
Terrain-perception-free Quadrupedal Spinning Locomotion on Versatile Terrains: Modeling, Analysis, and Experimental Validation
Hongwu Zhu, Dong Wang, Nathan Boyd +6
Dynamic quadrupedal locomotion over rough terrains reveals remarkable progress over the last few decades. Small-scale quadruped robots are adequately flexible and adaptable to trav…
Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning
Pan Lu, Ran Gong, Shibiao Jiang +4
Geometry problem solving has attracted much attention in the NLP community recently. The task is challenging as it requires abstract problem understanding and symbolic reasoning wi…
Synthesizing Diverse and Physically Stable Grasps with Arbitrary Hand Structures using Differentiable Force Closure Estimator
Tengyu Liu, Zeyu Liu, Ziyuan Jiao +2
Existing grasp synthesis methods are either analytical or data-driven. The former one is oftentimes limited to specific application scope. The latter one depends heavily on demonst…
Show Me What You Can Do: Capability Calibration on Reachable Workspace for Human-Robot Collaboration
Xiaofeng Gao, Luyao Yuan, Tianmin Shu +2
Aligning humans' assessment of what a robot can do with its true capability is crucial for establishing a common ground between human and robot partners when they collaborate on a…